Inspect AI vs DeepEval in 2026: how the two open source eval frameworks differ on who runs the model, scoring, agents, sandboxes, CI and which team each one suits.
How to use MMLU benchmark runs: pick the right variant, run it with lm-evaluation-harness or Inspect, smoke test the format, and read the score honestly.
MMLU vs GPQA explained: broad exam recall versus expert written science questions, why GPQA scores run lower, and which benchmark still means anything in 2026.
MMLU vs SWE-bench explained: what each measures, why the scores are not comparable, and which benchmark to trust for knowledge vs coding agents in 2026.
How to evaluate LLM model performance before you pick one: build a task test set, fix settings, add error bars, test prompt sensitivity, then weigh cost.
MMLU vs MMLU Pro explained: how the 4 option and 10 option benchmarks differ, why their scores aren't comparable, and which one to trust for LLMs in 2026.